Multi-lane space-time trajectory optimization method for intelligent network connection vehicle

Abstract

The invention provides a multi-lane space-time trajectory optimization method for an intelligent network connection vehicle. The space-time trajectory optimization algorithm based on a reinforcement learning algorithm is designed, so the optimal trajectory can be quickly matched. The algorithm comprises the following steps of (1) optimizing a space-time track, taking the current position and speedof a vehicle and a target driving-out lane, taking a time period as an input, and taking a set of vehicle accelerations as an output; and (2) optimizing multi-lane cooperative lane changing, taking the current position and speed of the vehicle and the position and speed threatening the vehicle of the target lane as input, and taking a vehicle acceleration set as output. That is to say, after a vehicle initiates a lane changing request, the track of the vehicle cooperative lane changing process can be matched through reinforcement learning, and after lane changing is completed, the space-timetrack at the moment is matched through reinforcement learning to achieve the multi-lane track optimization process. The method can optimize and generate the space-time track of the passing vehicles inthe road section in real time according to different road environments and traffic states, improves the mutual cooperation capability of the vehicles, improves safety of the passing road section andvehicle passing efficiency of the intersection, reduces the energy consumption level of the vehicles, and improves traffic safety of the road section in order to guarantee the traffic safety of the road; and a new solution and a theoretical basis are provided for improving the travel efficiency.

Description

technical field [0001] The invention belongs to the technical field of vehicle-road coordination / arterial traffic flow control, and specifically relates to a multi-lane space-time trajectory optimization method for intelligent networked vehicles, which is applicable to any signalized intersection section in an urban road traffic network. Background technique [0002] For the urban traffic road network, the current urban traffic road system controls the traffic flow at the intersection, except for a small number of suburban intersections with small traffic volumes that adopt the self-organizing control method without signals. The intersection is the main node connecting each road section, and the reasonable planning of traffic flow in each road section is also an important part of improving the traffic efficiency of the intersection. The driving behavior of the vehicle during the driving process of the road section can be divided into lane keeping behavior and lane changing b...

AI Technical Summary

Problems solved by technology

Compared with the single-lane space-time trajectory optimization problem, the multi-lane space-time trajectory optimization problem has higher complexity, and it is difficult to directly solve the multi-lane space-time trajectory optimization problem through the existing lane changing rules

[0013] 2. Traditional routing methods have limited information acquisition capabilities, often rely on fixed traffic detectors, and lack real-time, efficient, and accurate vehicle driving information and traffic status information

[0014] 3. Most of the traditional routing methods do not consider the factor of multi-vehicle coordinated lane change, and lack of consideration of the interference factors of surrounding vehicles

Establishing a mathematical model for the existing lane-changing rules is difficult to meet the multi-vehicle goal at the same time; for the mathematical calculation of the system, it is difficult to process a large amount of data of multiple vehicles at the same time

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